Combining surface characteristics and rock-mechanical properties to identify unstable glacier headwalls on a regional scale

2021 ◽  
Author(s):  
Andreas Ewald ◽  
Jan-Christoph Otto ◽  
Christoph von Hagke ◽  
Andreas Lang
Keyword(s):  
Mechanical Properties ◽  
Regional Scale ◽  
Surface Characteristics ◽  
Slope Instability ◽  
Glacier Retreat ◽  
Slope Aspect ◽  
Site Specific ◽  
Permafrost Rock ◽  
Landform Classification ◽  
Rock Mechanical Properties

<p>Global warming triggered retreat of alpine glaciers exposes large surface areas in the proglacial zone but also a significant headwall area above. The thermal and mechanical changes in the headwalls foster destabilisation and trigger rockfalls. Patterns of headwall destabilisation are complex due to variable rock strength and external atmospheric forcing and results are usually site-specific and do not allow regional scale stability assessments.</p><p>In order to understand sensitivity of alpine rock walls to instability following glacier retreat on a regional scale, we classify glacier headwalls based on a combination of surface and rock-mechanical characteristics. This includes (i) a semi-automatic detection of glacier headwalls using object-based image analysis, (ii) a morphometric analysis of headwalls, (iii) a regionalisation of rock-mechanical properties of the bedrock, and (iv) an analysis of other site conditions like potential permafrost occurrence and glacier retreat. We apply this workflow in the Hohe Tauern Range, Austria, to identify headwalls in recently deglaciated cirques and valleys with the highest potential for increased slope instability and rock fall processes.</p><p>For the central Hohe Tauern Range high-resolution digital datasets of topography, geology, glacier extent, and permafrost distribution are available. eCognition was used for semi-automatic headwall detection. Segmentation is derived from DEM derivatives like slope, aspect and a TPI-based landform classification. Headwall segments are classified based on slope and elevation thresholds that have been identified and validated using manual headwall mapping. Foliation information extracted from regional geological maps was compared to local geological surveys in order to specify type of foliation. Bedrock structure was interpolated based on a non-continuous azimuth distribution approach (NADIA). By combining topographic and geological data we derived a geotechnical classification scheme from cataclinal to anaclinal slopes with various dip-slope relations.</p><p>Preliminary results indicate that semi-automated headwall detection largely reproduces local observations. However, we observed an overestimation of 61% of total headwall area compared to the manually mapped headwalls. The rate of undetected areas is considered to be negligible. Overestimation mainly arises from inclusion of high-altitude profile straight slopes, matching the classification requirements without obvious glacial imprints such as schrundlines. Landform classification revealed a dominance of cataclinal slopes in the entire landscape. At steeper terrain, including glacier headwalls, anaclinal slopes prevail. Unstable situations such as overdip slopes are rare and predominantly found in the lower sections of glacier headwalls marked by schrundlines. Steep permafrost rock walls were found to be almost exclusively anaclinal, which might be considered as site-specific.</p><p>Our approach offers a new methodology to detect deglaciating headwalls and characterise their sensitivity to instability at a regional scale. Our classification can be used for up-scaling local headwall dynamics for a better anticipation of the destabilisation pattern of steep alpine slopes following glacier retreat.</p>

Towards a benchmark mechanical model for warming permafrost rock slopes

2020 ◽  
Author(s):  
Michael Krautblatter ◽  
Benjamin Jacobs ◽  
Philipp Mamot ◽  
Regina Pläsken ◽  
Riccardo Scandroglio ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Fracture Toughness ◽  
Mechanical Model ◽  
Rock Slope ◽  
Limit Equilibrium ◽  
Mechanical Parameters ◽  
Rock Slopes ◽  
Permafrost Rock ◽  
Mechanical Models ◽  
Rock Mechanical Properties

<p>This paper discusses mechanical modelling strategies for instable permafrost bedrock. Modelling instable permafrost bedrock is a key requirement to anticipate magnitudes and frequency of rock slope failures in a changing climate but also to forecast the stability of high-alpine infrastructure throughout its lifetime.  </p><p>High-alpine rock faces witness the past and present mechanical limit equilibrium. Rock segments where driving forces exceed resisting forces fall of the cliff often leaving a rock face behind which is just above the limit equilibrium. All significant changes in rock mechanical properties or significant changes in the state of stress will evoke rock instability which often occurs with response times of years to 1000 years. Degrading permafrost will act to alter (i) rock mechanical properties such as compressive and tensile strength, fracture toughness and most likely rock friction, (ii) warming subcero conditions will weaken ice and rock-ice interfaces and (iii) increased cryo- and (iv) hydrostatic pressures are expected. We have performed hundreds of laboratory experiments on different types of rock that show that thawing and warming siginficantly decreases both,  rock and ice-mechanical strength between -5°C and -0.0°C.  Approaches  to calculate cryostatic pressure (ad iii) have been published and are experimentally confirmed. However, the importance and dimension of extreme hydrostatic forces (ad iv) due to perched water above permafrost-affected rocks has been assumed but has not yet been quantitatively recorded.</p><p>This paper presents data and strategies how to obtain relevant (i) rock mechanical parameters (compressive and tensile strength and fracture toughness, lab), (ii) ice- and rock-ice interface mechanical parameters (lab), (iii) cryostatic forces in low-porosity alpine bedrock (lab and field) and (iv) hydrostatic forces in perched water-filled fractures above permafrost (field).</p><p>We demonstrate mechanical models that base on the conceptual assumption of the rock ice mechanical model (Krautblatter et al. 2013) and rely on frozen/unfrozen parameter testing in the lab and field. Continuum mechanical models (no discontinuities) can be used to demonstrate permafrost rock wall destabilization on a valley scale over longer time scales, as exemplified by progressive fjord rock slope failure in the Lateglacial and Holocene. Discontinuum mechanical models including rock fracture patterns can display rock instability induced by permafrost degradation on a singular slope scale, as exemplified for recent a recent ice-supported 10.000 m³ preparing rock at the Zugspitze (D). Discontinuum mechanical models also have capabilities to link permafrost slope stability to structural loading induced by high-alpine infrastructure such as cable cars and mountains huts, as exemplified for the Kitzsteinhorn Cable Car and its anchoring in permafrost rocks (A). </p><p>Over longer time scales, the polycyclicity of hydro- and cryostatic forcing as well as material fatigue play an important role. We also introduce a mechanical approach to quantify cryo-forcing related rock-fatigue. This paper shows benchmark approaches to develop mechanical models based on a rock-ice mechanical model for degrading permafrost rock slopes.</p>

scholarly journals Incorporation of Hybrid Nanomaterial in Dental Porcelains: Antimicrobial, Chemical, and Mechanical Properties

Antibiotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 98
Author(s):  
Carla L. Vidal ◽  
Izabela Ferreira ◽  
Paulo S. Ferreira ◽  
Mariana L. C. Valente ◽  
Ana B. V. Teixeira ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Fracture Toughness ◽  
Antimicrobial Activity ◽  
Biofilm Formation ◽  
Silver Ions ◽  
Surface Characteristics ◽  
Streptococcus Sobrinus ◽  
Hybrid Nanomaterial ◽  
Antimicrobial Substances ◽  
Health Area

Biofilm formation on biomaterials is a challenge in the health area. Antimicrobial substances based on nanomaterials have been proposed to solve this problem. The aim was to incorporate nanostructured silver vanadate decorated with silver nanoparticles (β-AgVO3) into dental porcelains (IPS Inline and Ex-3 Noritake), at concentrations of 2.5% and 5%, and evaluate the surface characteristics (by SEM/EDS), antimicrobial activity (against Streptococcus mutans, Streptococcus sobrinus, Aggregatibacter actinomycetemcomitans, and Pseudomonas aeruginosa), silver (Ag+) and vanadium (V4+/V5+) ions release, and mechanical properties (microhardness, roughness, and fracture toughness). The β-AgVO3 incorporation did not alter the porcelain’s components, reduced the S. mutans, S. sobrinus and A. actinomycetemcomitans viability, increased the fracture toughness of IPS Inline, the roughness for all groups, and did not affect the microhardness of the 5% group. Among all groups, IPS Inline 5% released more Ag+, and Ex-3 Noritake 2.5% released more V4+/V5+. It was concluded that the incorporation of β-AgVO3 into dental porcelains promoted antimicrobial activity against S. mutans, S. sobrinus, and A. actinomycetemcomitans (preventing biofilm formation), caused a higher release of vanadium than silver ions, and an adequate mechanical behavior was observed. However, the incorporation of β-AgVO3 did not reduce P. aeruginosa viability and increased the surface roughness of dental porcelains.

scholarly journals Quantifying the Variability of Forest Ecosystem Vulnerability in the Largest Water Tower Region Globally

2021 ◽  
Vol 18 (14) ◽  
pp. 7529
Author(s):  
Siqi Sun ◽  
Yihe Lü ◽  
Da Lü ◽  
Cong Wang
Keyword(s):  
Forest Ecosystems ◽  
Human Impacts ◽  
Regional Scale ◽  
Principal Component ◽  
Tibet Plateau ◽  
Slope Aspect ◽  
Environmental Disturbances ◽  
Restoration Planning ◽  
Development Goals ◽  
Qinghai Tibet Plateau

Forests are critical ecosystems for environmental regulation and ecological security maintenance, especially at high altitudes that exhibit sensitivity to climate change and human activities. The Qinghai-Tibet Plateau—the world’s largest water tower region—has been breeding many large rivers in Asia where forests play important roles in water regulation and water quality improvement. However, the vulnerability of these forest ecosystems at the regional scale is still largely unknown. Therefore, the aim of this research is to quantitatively assess the temporal–spatial variability of forest vulnerability on the Qinghai-Tibet Plateau to illustrate the capacity of forests to withstand disturbances. Geographic information system (GIS) and the spatial principal component analysis (SPCA) were used to develop a forest vulnerable index (FVI) to assess the vulnerability of forest ecosystems. This research incorporates 15 factors covering the natural context, environmental disturbances, and socioeconomic impact. Results indicate that the measure of vulnerability was unevenly distributed spatially across the study area, and the whole trend has intensified since 2000. The three factors that contribute the most to the vulnerability of natural contexts, environmental disturbances, and human impacts are slope aspect, landslides, and the distance to the farmland, respectively. The vulnerability is higher in forest areas with lower altitudes, steeper slopes, and southerly directions. These evaluation results can be helpful for forest management in high altitude water tower regions in the forms of forest conservation or restoration planning and implementation towards sustainable development goals.

Site-specific microstructure, porosity and mechanical properties of LENS™ processed Ti–6Al–4V alloy

2021 ◽  
pp. 141494
Author(s):  
Souvik Sahoo ◽  
Anuja Joshi ◽  
Vamsi K. Balla ◽  
Mitun Das ◽  
Shibayan Roy
Keyword(s):  
Mechanical Properties ◽  
Site Specific

scholarly journals Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

2021 ◽  
Vol 10 (5) ◽  
pp. 315
Author(s):  
Hilal Ahmad ◽  
Chen Ningsheng ◽  
Mahfuzur Rahman ◽  
Md Monirul Islam ◽  
Hamid Reza Pourghasemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Debris Flows ◽  
Regional Scale ◽  
Indus Basin ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Susceptibility Maps ◽  
Landslides And Debris Flows

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

Integration of Chemofacies and Rock Mechanical Properties Using Machine Learning Algorithms: Implications for Geomechanics and Hydraulic Fracture Stimulations in Paleozoic Formations, Saudi Arabia

2021 ◽  
Author(s):  
Maaruf Hussain ◽  
Abduljamiu Amao ◽  
Khalid Al-Ramadan ◽  
Sunday Olatunji ◽  
Ardiansyah Negara
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Saudi Arabia ◽  
Spatial Distribution ◽  
Chemical Composition ◽  
Rock Strength ◽  
Machine Learning Algorithms ◽  
Unconventional Reservoirs ◽  
Geochemical Properties ◽  
Rock Mechanical Properties

Abstract The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.

scholarly journals Approximate Dynamic Programming Based Control of Proppant Concentration in Hydraulic Fracturing

Mathematics ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 132 ◽  
Author(s):  
Harwinder Singh Sidhu ◽  
Prashanth Siddhamshetty ◽  
Joseph Kwon
Keyword(s):  
Mechanical Properties ◽  
Dynamic Programming ◽  
Hydraulic Fracturing ◽  
Young’S Modulus ◽  
Large Scale ◽  
Approximate Dynamic Programming ◽  
Young's Modulus ◽  
Computational Cost ◽  
Control Technique ◽  
Rock Mechanical Properties

Hydraulic fracturing has played a crucial role in enhancing the extraction of oil and gas from deep underground sources. The two main objectives of hydraulic fracturing are to produce fractures with a desired fracture geometry and to achieve the target proppant concentration inside the fracture. Recently, some efforts have been made to accomplish these objectives by the model predictive control (MPC) theory based on the assumption that the rock mechanical properties such as the Young’s modulus are known and spatially homogenous. However, this approach may not be optimal if there is an uncertainty in the rock mechanical properties. Furthermore, the computational requirements associated with the MPC approach to calculate the control moves at each sampling time can be significantly high when the underlying process dynamics is described by a nonlinear large-scale system. To address these issues, the current work proposes an approximate dynamic programming (ADP) based approach for the closed-loop control of hydraulic fracturing to achieve the target proppant concentration at the end of pumping. ADP is a model-based control technique which combines a high-fidelity simulation and function approximator to alleviate the “curse-of-dimensionality” associated with the traditional dynamic programming (DP) approach. A series of simulations results is provided to demonstrate the performance of the ADP-based controller in achieving the target proppant concentration at the end of pumping at a fraction of the computational cost required by MPC while handling the uncertainty in the Young’s modulus of the rock formation.

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